This role pays less than
55%
of similar roles. Most pay
$192,050–$246,150
— the shaded band above.
At the midpoint, this role pays about
$211k
versus about
$219k
for comparable roles.
Based on 240 similar postings.
Employer
About Capital One Financial
Capital One Financial is a bank holding company specializing in credit cards, auto loans, banking, and savings products, known for its data-driven approach to consumer and commercial finance. Industry: Financial Services & Banking
Capital One Financial currently has
998 open roles
on FindRole.
Listed pay typically runs
$197,300–$225,100
across 992 roles with salary data.
Lead AI/ML Engineer (Platform, kubeflow) joins the Intelligent Foundations and Experiences team to develop and deploy proprietary solutions that empower internal teams to enhance products with scalable, responsible AI. This role involves designing, testing, and supporting software components including foundation model training, large language model inference, similarity search, guardrails, model evaluation, experimentation, governance, and observability. The engineer will implement state-of-the-art LLM optimization techniques to improve performance metrics like scalability, cost, latency, and throughput for production systems. The technical stack includes Python, Go, Scala, Java, C++, C#, PyTorch, Huggingface, VectorDBs, Nemo Guardrails, and AWS Ultraclusters. The role focuses on the core challenge of building high-performance AI infrastructure to solve complex problems in banking technology, ensuring that large-scale production systems remain efficient while providing advanced capabilities for customer interactions and internal workflows.
Design, develop, test, deploy, and support AI software components including foundation model training and LLM inference.
Implement similarity search, guardrails, model evaluation, experimentation, governance, and observability for production systems.
Utilize a broad stack of open-source and SaaS technologies like Huggingface, VectorDBs, and PyTorch.
Develop state-of-the-art LLM optimization techniques to improve performance, scalability, cost, latency, and throughput.
Contribute to the technical vision and long-term roadmap of foundational AI systems at Capital One.
Translate complex scientific research into practical, production-ready AI engineering solutions.
What we're looking for
Bachelor's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus 4 years of experience developing AI/ML algorithms.
Master's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus 2 years of experience developing AI/ML algorithms.
At least 4 years of experience programming with Python, Go, Scala, or Java.
6 years of experience deploying scalable and responsible AI solutions on cloud platforms like AWS, Google Cloud, or Azure.
Experience designing, developing, delivering, and supporting AI services including LLM inference, similarity search, and vector databases.
Experience applying state-of-the-art techniques to optimize training and inference software for hardware utilization, latency, throughput, and cost.
Ability to interpret scientific publications and apply novel research techniques in production environments.
Strong foundation in engineering and mathematics to identify and exploit optimization opportunities in hardware and software.